Research and application of load forecasting method for park integrated energy system based on XGBoost algorithm

Abstract

Load forecasting is the basis of energy management and optimal dispatching of the park-integrated energy system, and its forecasting accuracy directly affects the overall operation performance of the system. Therefore, this paper proposes a load forecasting method for the park-integrated energy system based on the XGBoost algorithm. This paper first establishes the simulation analysis model of photovoltaic power generation and electric vehicle charging and discharging in the park and then carries out the supply and demand forecast and load characteristic modeling of the park’s energy system. Finally, this paper constructs the power prediction optimization control flow based on the XGBoost prediction model and uses the mean absolute percentage error (MAPE) and root mean square error (RMSE) as two evaluation indicators. The validity of the proposed method is verified by the data of the actual micro energy system in the park.